Concept tree based clustering visualization with shaded similarity matrices

Jun Wang, Bei Yu, Les Gasser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the problems with existing clustering methods is that the interpretation of clusters may be difficult. Two different approaches have been used to solve this problem: conceptual clustering in machine learning and clustering visualization in statistics and graphics. The purpose of this paper is to investigate the benefits of combining clustering visualization and conceptual clustering to obtain better cluster interpretations. In our research we have combined concept trees for conceptual clustering with shaded similarity matrices for visualization. Experimentation shows that the two interpretation approaches can complement each other to help us understand data better.

Original languageEnglish (US)
Title of host publicationProceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002
Pages697-700
Number of pages4
StatePublished - Dec 1 2002
Event2nd IEEE International Conference on Data Mining, ICDM '02 - Maebashi, Japan
Duration: Dec 9 2002Dec 12 2002

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other2nd IEEE International Conference on Data Mining, ICDM '02
CountryJapan
CityMaebashi
Period12/9/0212/12/02

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Keywords

  • Clustering visualization
  • Concept tree
  • Conceptual clustering
  • Shaded similarity matrix

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Wang, J., Yu, B., & Gasser, L. (2002). Concept tree based clustering visualization with shaded similarity matrices. In Proceedings - 2002 IEEE International Conference on Data Mining, ICDM 2002 (pp. 697-700). (Proceedings - IEEE International Conference on Data Mining, ICDM).